Conventional decision support tools for operational problems in the oil and gas industry separate planning and scheduling of activities within a facility into two distinct processes that take place in two different contexts. However, these two types of tasks are inherently interdependent, because the plan provides guidance for the implementation of the schedule and the schedule must represent real-world events while observing the business goals set out during the planning process.
Forming and executing a schedule is typically defined by operational events within a site, or by external factors that affect the operations within the site. In the context of time, scheduling decisions are based on moments when the events occur, more often than on preset time periods, such as days, weeks or months. Moreover, schedulers make decisions according to “aggregate” expectations during these events. In other words, certain events in the schedule are formulated in anticipation of future events that they affect or that they are influenced by. This interaction between elements of the schedule needs to be coordinated in the context of real-world feasibility as well as in view of economic metrics.
Planning, on the other hand, normally takes place in an entirely different framework of analysis than scheduling. Namely, planning is conducted in a single-period model, where given raw-material and product prices, crude receipts, refinery models and constraints determine average monthly quantities of products and intermediates and average operating modes and conditions. In other words, in contrast with the scheduling which focuses on achievability of tangible events, the planning tasks concentrate on the economic optimality, where time averaged profitability, for example, is set as a goal for each unit of time within the entire planning time period.
However, while the planning tasks join uniform segments of time containing the averaged data into a single-period model, the segments of time within the schedule, on the other hand, are generally event-based. Accordingly, the schedule is managed by a multi-period model, where, for example, inventories, product liftings or operating modes change weekly or daily. Moreover, the events of one time period of the schedule affect the subsequent ones, thereby further diminishing their mutual uniformity.
These divergent characteristics of the single-period model of the planning and the multiple-period model of the scheduling create difficulties in aligning a feasible schedule with the planning related expectations.
In light of the discussed problems of the existing technology, there is a need for a tool that is capable of bringing the planning and scheduling models closer together. Specifically, there is need for a technique that would set one of the two time models as a reference and derive the other one accordingly by providing a degree of flexibility to the derived one so that it can be adjusted based on the referenced one.